Challenges exist in the endeavor of dynamic cognitive query processing on a large volume of pre-processed data. For example, consider a scenario wherein a user is watching a video lecture. The user may want to pause the video and ask a question regarding certain information presented to the user in the video, wherein the answer to the user's question may not be contained in the video. However, in such environments, there is no teacher or instructor available to respond to the user's question, as the information flow is one-directional from the backend server to the user. The user could, for instance, type a question into an independent search engine, and that search engine may provide a response, regardless of the video content in the lecture. However, such a response would not share and/or process the content that the user is receiving via the video lecture, nor would the response incorporate the context associated with the processing of the presented content. Alternatively, having a human available in conjunction with the video lecture to serve as a teacher can be cost prohibitive in terms of scaling to a potentially large number of users in such environments (such as, for example, online environments).